CN103152820B - A kind of wireless sensor network acoustic target iteration localization method - Google Patents

Abstract

The invention discloses a kind of wireless sensor network acoustic target iteration localization method.Described method utilizes some wireless sound sensor node modules and aggregation node to form target localization wireless sensor network, based on the target sound signal strength data that sonic transducer node receives, utilize the probability distribution of particle filter iterative estimate target location parameter, receive target location and estimate.The present invention adopts Auxiliary Particle Filter device to carry out iterative estimate in conjunction with Gaussian Mixture core smoothing method to target location, aggregation node only processes the observation data of a sensor node at every turn, thus breach bandwidth restriction and the frequency constraint of wireless sensor network, and reduce the energy ezpenditure of network.The present invention can obtain higher target location accuracy by multi-sensor information fusion, and under the restriction of the physics such as sensor network energy and bandwidth, can meet the requirement of real-time of target localization.

Description

A kind of wireless sensor network acoustic target iteration localization method

Technical field

The present invention relates to wireless sensor network technology field, particularly a kind of new wireless sensor network iteration acoustic target localization method.

Background technology

Wireless sensor network is made up of the sensor node of low cost dense deployment, integrates perception and wireless telecommunications.The application of wireless sensor network target location comprises battlefield surveillance, logistics management, security protection etc., has the method for a lot of target localization to be suggested at present both at home and abroad.

In the world, X.Sheng etc. propose the maximum likelihood auditory localization algorithm under wireless sensor network, but the method needs a large amount of network communications and calculating, because aggregation node only has limited bandwidth sum energy, and the numerical algorithm of maximal possibility estimation is difficult to convergence, therefore the method in practice and infeasible (document 1:X.Sheng, and Y.H.Hu, " Maximum likelihood multiple-source localization usingacoustic energy measurements with wireless sensor networks, " IEEE Transactions onSignal Processing, vol.53, no.1, pp.44 – 53, 2005.).E.Masazade etc. propose the target location algorithm of the iteration based on particle filter, but in the algorithm, particle filter is all sample from the prior distribution of a known distribution at every turn, cause the degeneration of filter and disperse, bring larger position error (document 2:E.Masazade, R.Niu, P.K.Varshney, M.Keskinoz.Energy aware iterativesource localization for wireless sensor networks.IEEE Transactions on SignalProcessing, 2010, 58 (9): 4824-4835.).Chongqing Mail and Telephones Unvi He Wei person of outstanding talent etc. sets up a kind of measurement information blending algorithm based on particle filter and positions target under Bayesian frame, but under WSN, sensor node is not precise synchronization, therefore be difficult to obtain TOA and TDOA and measure (document 3: He Weijun, Zhou Fei, " TOA/TDOA based on particle filter merges location algorithm research ", sensing technology journal, 2010).

Domestic applications number are that the patent utilization statistical learning method of CN200810225565.4 proposes a kind of wireless sensor network target location and tracking, the domestic patent No. is that the patent of CN200910201284.X proposes a kind of mobile robot's particle localization method, utilizes Unscented kalman filtering and particle filter to estimate robot location.The domestic patent No. is that the patent of CN201110435631.2 proposes a kind of particle state evaluation method, overcomes because filtering starting stage prior information is not enough the problem that the initial prior state of particle carries out mistake estimation and then causes filtering instability even to be dispersed.The domestic patent No. is that the patent of CN200910078474.7 proposes a kind of target localization and tracking system and method, adopts multisensor to realize the locating and tracking to target to the observation of target bearing.

Above-mentioned existing methodical common drawback is: operand is large, and location algorithm is degenerated relatively more serious, causes position error comparatively large, location inaccuracy.

Summary of the invention

The defect existed for above-mentioned prior art and deficiency, the present invention proposes a kind of wireless sensor network iteration acoustic target localization method, breaks through wireless sensor network communication bandwidth, frequency and energy quantitative limitation, improves the precision of target localization.

To achieve these goals, the present invention takes following technical solution:

A kind of wireless sensor network acoustic target iteration localization method, some wireless sound sensor node modules and aggregation node is utilized to form target localization wireless sensor network, when transducer receives acoustic target signal strength data, utilize the probability distribution of particle filter iterative estimate target unknown parameter, thus obtain target location estimation.

As the preferred embodiments of the present invention, the method for the probability distribution of particle filter iterative estimate target unknown parameter is utilized to comprise the following steps:

(2.1) unknown parameter θ=[P is established ₀, x, y] and comprise unknown sound-source signal intensity parameters P ₀with target location coordinate (x, y); At primary iteration i=0, from interval [0, P _m] go up in non-uniform probability distribution function the sound-source signal intensity P that samples ₀sample, P _mfor the maximum of voice signal, in whole wireless monitor region, the sample of sampled targets position (x, y) in non-uniform probability distribution function, obtains M target unknown parameter sample θ ₀ ^(m), m=1,2 ..., M, distributes equal weights to each particle obtain initial sample set { θ ₀ ^(m), w ₀ ^(m)| m=1 ..., M};

(2.2) in successive iterations step, aggregation node is each sensor node of sequential activation successively; Be located in the i-th+1 time iteration, node i+1 target sound intensity data observed that is activated passes to aggregation node, aggregation node utilize monte carlo method from set 1,2 ..., resampling one group of M auxiliary variable m in M} ^l∈ 1,2 ..., M}, sampled probability is:

$p(m^l=m)\∝p\left(z_{i+l}\right|{\mathrm{\μ}}_i^{\left(m\right)})w_i^{\left(m\right)},m=1,\·\·\·,M$

Wherein: for the average of m kernel function in Gaussian Mixture distribution, constant α and b represents the contraction that Gaussian Mixture distributes and degree of scatter respectively, and its value depends on compromise factor delta,

$\mathrm{\α}=\sqrt{{1-b}^2},b^2=1-{[(3\mathrm{\δ}-1)/2\mathrm{\δ}]}^2,$ 0.95≤δ≤0.99， $\stackrel{\‾}{{\mathrm{\θ}}_i}={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}{\mathrm{\θ}}_i^m$ For the sample set { θ that last iteration obtains _i ^(m), w _i ^(m)m=1 ..., the average of M}, wherein, θ _i ^(m)and w _i ^(m)be respectively unknown parameter sample and weights thereof in i-th iteration, for sensor node observation data z _i+1likelihood score; (2.3) based on Gaussian Mixture core smoothing method, aggregation node utilizes monte carlo method from M Gaussian component of kernel density function middle sampling respectively obtains M new target component sample wherein: it is μ that N (μ, V) represents average, covariance matrix is the Gaussian Profile probability density function of V, m ^lfor the auxiliary variable obtained in step (2.2), for sample set { θ _i ^(m), w _i ^(m)| m=1 ..., the m in M} ^lindividual sample, for m in Gaussian Mixture distribution ^lthe average of individual kernel function, for sample set { θ _i ^(m), w _i ^(m)m=1 ..., the covariance matrix of M}, for the Gaussian component in Gaussian Mixture distribution kernel function, aggregation node utilizes the observation data z of sensor node i+1 _i+1calculate the weights of each sample obtain one group of weighted sample set { θ _i+1 ^(m), w _i+1 ^(m)| m=1 ..., M};

(2.4) aggregation node utilizes the weighted sample set { θ obtained _i+1 ^(m), w _i+1 ^(m)| m=1 ..., M}, iteration meter

Calculate target component estimated value: ${\widehat{\mathrm{\θ}}}_{i+1}=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{w}_{i+1}^{\left(m\right)}{\mathrm{\θ}}_{i+1}^{\left(m\right)};$

(2.5) above-mentioned steps (2.1) is repeated to (2.4), until traveled through all the sensors node.

As the preferred embodiments of the present invention, be located in i-th iteration, the weighted sample collection that aggregation node obtains is { θ _i ^(m), w _i ^(m)m=1 ..., M}, utilizes Gaussian Mixture core smoothing method, and in i-th iteration, the probability distribution of target unknown parameter θ can be expressed as Gaussian Mixture distribution:

As the preferred embodiments of the present invention, described target component sample the computing formula of weights is:

$w_{i+1}^{\left(m\right)}\∝\frac{p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})}{p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m^l\right)})},$

Wherein, $p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\θ}}_{i+1}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$p\left(z_{i+1}\right|u_i^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left(u_i^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

σ is the covariance of sensors observe noise, $a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$ (s _xi, s _yi) and (x, y) be respectively the coordinate of i-th sensor node and acoustic target.

As the preferred embodiments of the present invention, before target localization, first set up the Mathematical Modeling that signal strength signal intensity decays with target range:

$a_i^2=\frac{G_i{P}_0^{\′}}{{\left(\frac{d_i}{d_0}\right)}^n},$

Wherein, a _irepresent the sound-source signal intensity that i-th sonic transducer node receives, G _ithe gain of i-th sensor node, P ₀' be at reference distance d ₀the intensity of place's sound source, d _ifor the distance of target and i-th sensor node, (s _xi, s _yi) and (x, y) be respectively the position coordinates of i-th sensor node and target, n=2 is acoustic signal intensity damped expoential.

As the preferred embodiments of the present invention, aggregation node carries out Sequential processing to each nodes see data, and in each iteration, aggregation node only processes the observation data of a sensor node, other sensor node processes resting states.

Compared with traditional maximum Likelihood, the present invention adopts Auxiliary Particle Filter device and Gaussian Mixture core smoothing method to carry out iterative estimate to target location, aggregation node only need process the observation data of a sensor node at every turn, thus breach bandwidth restriction and the frequency constraint of wireless sensor network, save the energy ezpenditure of network.Compared with traditional object localization method, the present invention obtains higher target location accuracy by the information fusion of multisensor, and under the restriction of the physics such as sensor network energy and bandwidth, can meet the requirement of real-time of target localization.

Accompanying drawing explanation

Fig. 1 is the acoustic target localization method flow chart of wireless sensor network of the present invention;

Fig. 2 is the deployment of wireless sensor node in monitored area and the actual position schematic diagram of target;

Fig. 3 is the design sketch utilizing object localization method of the present invention to carry out iteration location.

Fig. 4 is root-mean-square error (RMSE) figure utilizing object localization method of the present invention to estimate target location.

Embodiment

Wireless sensor network acoustic target iteration localization method involved in the present invention, comprises the following steps:

Steps A, set up the Mathematical Modeling that sound signal intensity is decayed with target range:

$a_i^2=\frac{G_i{P}_0^{\′}}{{\left(\frac{d_i}{d_0}\right)}^n}---\left(1\right)$

Wherein a _irepresent the sound-source signal intensity that i-th sonic transducer node receives, G _ithe gain of i-th sensor node, P ₀' be at reference distance d ₀the intensity of place's sound source, d _ifor the distance of target and i-th sensor node, (s _xi, s _yi) and (x, y) be respectively the coordinate of i-th sensor node and acoustic target, n=2 is acoustical signal damped expoential.For simplicity, suppose all transducer G _i=G, makes P ₀=GP ₀', d ₀=1, then sound-source signal Strength degradation model can be reduced to

$a_i^2=\frac{P_0}{d_i^2}---\left(2\right)$

Take into account modeling error and background noise, i-th transducer actual observation to the acoustic signal intensity measured value from target be:

$z_i=a_i+w_i---\left(3\right)$

Here noise w is supposed _iindependent identically distributed to all sensor nodes, and Gaussian distributed, i.e. w _i~ N (0, σ ²), σ is the covariance seeing then noise.Note target unknown parameter θ=[P ₀, x, y], three components comprise unknown sound-source signal intensity parameters P to be estimated ₀with target location coordinate (x, y).

Step B, based on Auxiliary Particle Filter device, auxiliary variable of sampling from particle assembly.

At iteration start time i=0, from interval [0, P _m] (P _mmaximum for sound-source signal) go up in non-uniform probability distribution function the sound-source signal intensity P that samples ₀sample, in whole wireless monitor region, the sample of sampled targets position coordinates (x, y) in non-uniform probability distribution function, obtains M sample particles θ ₀ ^(m), and distribute equal weights obtain initial sample set { θ ₀ ^(m), w ₀ ^(m)| m=1 ..., M}.

In successive iterations, aggregation node activates a sensor node at every turn, and in order to conserve energy, all the other nodes are in resting state.Be located in i+1 iteration, aggregation node activated sensors node i+1, the target sound intensity data that sensor node i+1 is observed passes to aggregation node.Aggregation node first utilize monte carlo method from set 1,2 ..., sample in M} one group of M auxiliary variable m ^l, sampled probability is:

$p(m^l=m)\∝p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m\right)}),m=1,\·\·\·,M$

Wherein: for average (the document 4:LiuJ of m kernel function in Gaussian Mixture distribution, West M.Combined parameter and state estimation in simulation-based filtering.Sequential Monte Carlo Methods in Practice.New York:Springer, 2001.197-224) for the sample set { θ that last iteration obtains _i ^(m), w _i ^(m)| m=1 ..., the average of M}, for sensor node observation data z _i+1likelihood score:

$p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\μ}}_i^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2})$

Wherein: parameter σ is the covariance that in formula (3), then noise seen by transducer,

wherein parameter [P ₀, x, y] and=μ _i ^(m), namely get μ _i ^(m)three components.

Step C, based on Gaussian Mixture core smoothing method, the acoustic target parameter sample that aggregation node utilizes monte carlo method to sample new respectively from the Gaussian component of cuclear density, concrete grammar is as follows:

Use the parameter of the sample calculation Gaussian Mixture kernel function of last iteration:

$\stackrel{\‾}{{\mathrm{\θ}}_i}={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}{\mathrm{\θ}}_i^m$

$V_i={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}({{\mathrm{\θ}}_i}^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i}){({{\mathrm{\θ}}_i}^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i})}^T$

${\mathrm{\μ}}_i^{\left(m^l\right)}={{\mathrm{\α\θ}}_i}^{\left(m^l\right)}+(1-\mathrm{\α})\stackrel{\‾}{{\mathrm{\θ}}_i}$

Wherein m ^lfor the auxiliary variable obtained in step B, then from the Gaussian component of kernel density function middle sampling obtains new target component sample m=1,2 ..., M.

Step D, aggregation node utilizes the observation data z of the sensor node i+1 received _i+1the weights of each sample of iterative computation normalization:

$w_{i+1}^{\left(m\right)}\∝\frac{p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})}{p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m^l\right)})}$

Wherein: $p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\θ}}_{i+1}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$a_{i+1}\left({{\mathrm{\θ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$

$p\left(z_{i+1}\right|u_i^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left(u_i^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$ Wherein parameter [P ₀, x, y] and=μ _i ^(m).

Step e, aggregation node utilizes the weighted sample set obtained the estimates of parameters of iterative computation target: ${\widehat{\mathrm{\θ}}}_{i+1}=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{w}_{i+1}^{\left(m\right)}{\mathrm{\θ}}_{i+1}^{\left(m\right)}.$

Proceed to step B, enter next iterative cycles, until traveled through all the sensors node.

The invention has the beneficial effects as follows: the present invention proposes a kind of wireless sensor network acoustic target iteration localization method, sensors observe pattern is simple, has less amount of calculation, low to communication bandwidth sum frequency requirement, can obtain higher positioning precision.Utilize Auxiliary Particle Filter device and Gaussian Mixture core smoothing method, effectively can overcome the degradation phenomena of particle filter, accelerate filter converges, meet the requirement of real-time of wireless sensor network target location.

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with accompanying drawing and example, wireless sensor network acoustic target iteration localization method of the present invention is described in detail further.

See Fig. 1, be wireless sensor network acoustic target iteration localization method flow chart, the method mainly comprises the following steps:

Step S101, at iteration start time i=0, from interval [0, P _m] (P _mmaximum for sound-source signal) go up in non-uniform probability distribution function the sound-source signal intensity P that samples ₀sample, in whole wireless monitor region, the sample of sampled targets position coordinates (x, y) in non-uniform probability distribution function, obtains M sample particles θ ₀ ^(m), and distribute equal weights obtain initial sample set { θ ₀ ^(m), w ₀ ^(m)| m=1 ..., M}.

In successive iterations, each sensor node of the sequential activation of aggregation node detects, and in order to conserve energy, all the other nodes are in resting state.In i-th iteration, the weighted sample collection that particle filter obtains is { θ _i ^(m), w _i ^(m)| m=1 ..., M}.Utilize Gaussian Mixture core smoothing method, in i-th iteration, the probability distribution of target unknown parameter θ can be expressed as Gaussian Mixture and distribution:

$\hat{p}\left(\mathrm{\θ}\right)={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}N({\mathrm{\μ}}_i^{\left(m\right)},b^2{V}_i)$

Wherein: it is μ that N (μ, V) represents average, covariance matrix is the Gaussian distribution density function of V,

${\mathrm{\μ}}_i^{\left(m\right)}={\mathrm{\α\θ}}_i^{\left(m\right)}+(1-\mathrm{\α})\stackrel{\‾}{{\mathrm{\θ}}_i},\stackrel{\‾}{{\mathrm{\θ}}_i}={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}{\mathrm{\θ}}_i^{\left(m\right)},$ $V_i={\mathrm{\Σ}}_{m=1}^M{w}_i^{\left(m\right)}({\mathrm{\θ}}_i^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i}){({\mathrm{\θ}}_i^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i})}^T,$ Constant α and b represents contraction and the degree of scatter of Gaussian Mixture respectively, and its value depends on the compromise factor 0.95≤δ≤0.99, and $\mathrm{\α}=\sqrt{{1-b}^2},$ $b^2=1-{[(3\mathrm{\δ}-1)/2\mathrm{\δ}]}^2,$ $N({\mathrm{\μ}}_i^{\left(m\right)},b^2{V}_i)$ For the Gaussian component kernel function in Gaussian Mixture distribution.

Step S102, be located in the i-th+1 time iteration, sensor node i+1 is activated, and the target sound intensity data observed passes to aggregation node.Aggregation node first utilize monte carlo method from set 1,2 ..., resampling one group of M auxiliary variable m in M} ^l, sampled probability is:

$p(m^l=m)\∝p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m\right)})w_i^{\left(m\right)},m=1,\·\·\·,M$

Wherein: for the sample set { θ that last iteration obtains _i ^(m), w _i ^(m)| m=1 ..., the average of M}, for the average of m kernel function in Gaussian Mixture distribution, for sensor node observation data z _i+1likelihood score,

$p\left(z_{i+1}\right|{{\mathrm{\μ}}_i}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2})$

Wherein: parameter σ is the covariance that in formula (3), then noise seen by transducer,

$a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$ Wherein parameter [P ₀, x, y] and=μ _i ^(m).

Step S103, utilizes monte carlo method difference sampled targets parameter sample from the Gaussian component of cuclear density, specific as follows:

The sample set of last iteration is used to calculate the parameter of Gaussian Mixture kernel function:

$\stackrel{\‾}{{\mathrm{\θ}}_i}={\mathrm{\Σ}}_{m=1}^M{w_i}^{\left(m\right)}{{\mathrm{\θ}}_i}^{\left(m\right)}$

$V_i={\mathrm{\Σ}}_{m=1}^M{w_i}^{\left(m\right)}({{\mathrm{\θ}}_i}^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i}){({{\mathrm{\θ}}_i}^{\left(m\right)}-\stackrel{\‾}{{\mathrm{\θ}}_i})}^T,$

${{\mathrm{\μ}}_i}^{\left(m^l\right)}={{\mathrm{\α\θ}}_i}^{\left(m^l\right)}+(1-\mathrm{\α})\stackrel{\‾}{{\mathrm{\θ}}_i},$

Wherein: m ^lfor the auxiliary variable obtained in previous step, for the average of sample, V _ifor sample variance.Thus obtain M Gaussian component kernel density function from each Gaussian component middle sampling respectively obtains the sample of new target component m=1 ..., M.

Step S104, calculates the sample of target component weights and normalization:

$w_{i+1}^{\left(m\right)}\∝\frac{p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})}{p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m^l\right)})}$

Its Middle molecule likelihood function: $p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\θ}}_{i+1}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$a_{i+1}\left({{\mathrm{\θ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$ Wherein parameter [P ₀, x, y] and=θ _i+1 ^(m).

Denominator likelihood function: $p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\μ}}_i^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right)=\sqrt{\frac{P_0}{{(s_{\mathrm{xi}}-x)}^2+{(s_{\mathrm{yi}}-y)}^2}},$ Wherein parameter [P ₀, x, y] and=μ _i ^(m).

Step S105, aggregation node utilizes the weighted sample set obtained, and obtains the estimates of parameters of target in this iteration: ${\widehat{\mathrm{\θ}}}_{i+1}=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{\stackrel{\‾}{w}}_{i+1}^{\left(m\right)}{\mathrm{\θ}}_{i+1}^{\left(m\right)}.$

Step S106, repeat step S101 ~ S105, iterative estimate target unknown parameter, until traveled through all the sensors node.

Below with example in detail wireless sensor network acoustic target of the present invention iteration localization method:

As shown in Figure 2, if 25 microphone sonic transducer nodes are evenly deployed in 100 × 100m ²in square region, the position of each sensor node is obtained by self-contained GPS, and the actual position coordinate of acoustic target is (60,60), and we utilize the observation data of institute's voicing sensor node to position sound source.

In order to save energy, the bandwidth sum frequency of aggregation node, extend the Web vector graphic life-span, aggregation node carries out Sequential processing to observation data, each only activation sensor node, all the other nodes are in resting state, until traveled through all the sensors node, iteration has terminated, and completes the location to target.

If the number of samples of particle filter is M=2000, sensor node observation noise variances sigma ²=1.In primary iteration, sound-source signal intensity P ₀initial sample come from and be uniformly distributed U [0,500] (U [a, b] [a between Representative Region, b] on non-uniform probability distribution function), the initial sample of target location coordinate (x, y) comes from two dimensional uniform distribution U [(0,100), (0,100)], and uniform distribution weights are w ₀ ^(m)=1/2000.According to acoustic target localization method of the present invention, in whole iterative location process to the effect of target location estimation as shown in Figure 3.

Through 50 Monte Carlo simulations, as shown in Figure 4, as can be seen from the figure, along with the increase of iterations, error is more and more less, achieves accurate target localization for the root-mean-square error (RMSE) that target location is estimated.

Be described specific embodiment of the invention above and illustrate, this example is only the present invention's preferably embodiment, and is not used in and limits the invention, and the present invention should make an explanation according to appended claim.

Claims (4)

1. a wireless sensor network acoustic target iteration localization method, it is characterized in that, some wireless sound sensor node modules and aggregation node is utilized to form target localization wireless sensor network, when sensor node receives acoustic target signal strength data, utilize the probability distribution of particle filter iterative estimate target unknown parameter, thus obtain target location estimation;

The method of the probability distribution of particle filter iterative estimate target unknown parameter is utilized to comprise the following steps:

(1.1) unknown parameter q=[P is established ₀, x, y] and comprise unknown sound-source signal intensity parameters P ₀with target location coordinate (x, y); At primary iteration i=0, from interval [0, P _m] go up in non-uniform probability distribution function the sound-source signal intensity P that samples ₀sample, P _mfor the maximum of voice signal, in whole wireless monitor region, the sample of sampled targets position (x, y) in non-uniform probability distribution function, obtains M target unknown parameter sample m=1,2 ..., M, distributes equal weights to each particle $w_0^{\left(m\right)}=1/M,$ Obtain initial sample set $\{{{\mathrm{\θ}}_0}^{\left(m\right)},{w_0}^{\left(m\right)}|m=1,...,M\};$

(1.2) in successive iterations step, aggregation node is each sensor node of sequential activation successively; Be located in the i-th+1 time iteration, node i+1 target sound intensity data observed that is activated passes to aggregation node, aggregation node utilize monte carlo method from set 1,2 ..., resampling one group of M auxiliary variable m in M} ^l∈ 1,2 ..., M}, sampled probability is:

$p(m^l=m)\∝p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m\right)})w_i^{\left(m\right)},m=1,...,M$

Wherein: for the average of m kernel function in Gaussian Mixture distribution, constant α and b represents the contraction that Gaussian Mixture distributes and degree of scatter respectively, and its value depends on compromise factor delta, b ²=1-[(3 δ-1)/2 δ] ², 0.95≤δ≤0.99, for the sample set that last iteration obtains average, wherein, with be respectively unknown parameter sample and weights thereof in i-th iteration, for sensor node observation data z _i+1likelihood score;

(1.3) based on Gaussian Mixture core smoothing method, aggregation node utilizes monte carlo method from M Gaussian component of kernel density function middle sampling respectively obtains M new target component sample wherein: it is μ that N (μ, V) represents average, covariance matrix is the Gaussian Profile probability density function of V, m ^lfor the auxiliary variable obtained in step (1.2), for the average of Gaussian component, for sample set in m ^lindividual sample, for m in Gaussian Mixture distribution ^lthe average of individual kernel function, $V_i={\mathrm{\Σ}}_{m=1}^M{w_i}^{\left(m\right)}({{\mathrm{\θ}}_i}^m-{\stackrel{\‾}{\mathrm{\θ}}}_i){({{\mathrm{\θ}}_i}^m-{\stackrel{\‾}{\mathrm{\θ}}}_i)}^T$ For sample set $\{{{\mathrm{\θ}}_i}^{\left(m\right)},{w_i}^{\left(m\right)}|m=1,...,M\}$ Covariance matrix, for the Gaussian component in Gaussian Mixture distribution kernel function, aggregation node utilizes the observation data z of sensor node i+1 _i+1calculate the weights of each sample obtain one group of weighted sample set $\{{{\mathrm{\θ}}_{i+1}}^m,{w_{i+1}}^{\left(m\right)}|m=1,...,M\};$

(1.4) aggregation node utilizes the weighted sample set obtained iterative computation target component estimated value:

(1.5) above-mentioned steps (2.1) is repeated to (2.4), until traveled through all the sensors node; Described target component sample the computing formula of weights is:

$w_{i+1}^{\left(m\right)}\∝\frac{p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})}{p\left(z_{i+1}\right|{\mathrm{\μ}}_i^{\left(m^l\right)})},$

Wherein: $p\left(z_{i+1}\right|{\mathrm{\θ}}_{i+1}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({\mathrm{\θ}}_{i+1}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

$p\left(z_{i+1}\right|{{\mathrm{\μ}}_i}^{\left(m\right)})=\frac{1}{\sqrt{2\mathrm{\π\σ}}}\exp (-\frac{{(z_{i+1}-a_{i+1}\left({{\mathrm{\μ}}_i}^{\left(m\right)}\right))}^2}{{2\mathrm{\σ}}^2}),$

σ is the covariance of sensors observe noise, (s _xi, s _yi) and (x, y) be respectively i-th sensor node and parameter μ _i ^(m)the acoustic target coordinate parameters of middle correspondence, definition be similar to

2. a kind of wireless sensor network acoustic target iteration localization method as claimed in claim 1, it is characterized in that, be located in i-th iteration, the weighted sample collection that aggregation node obtains is utilize Gaussian Mixture core smoothing method, in i-th iteration, the probability distribution of target unknown parameter θ can be expressed as Gaussian Mixture distribution:

$\hat{p}\left(\mathrm{\θ}\right)={\mathrm{\Σ}}_{m-1}^M{w}_i^{\left(m\right)}N({\mathrm{\μ}}_i^{\left(m\right)},b^2{V}_i).$

3. a kind of wireless sensor network acoustic target iteration localization method as claimed in claim 1, is characterized in that, before target localization, first set up the Mathematical Modeling that sound signal intensity is decayed with target range:

$a_i^2=\frac{G_i{P}_0^{\′}}{{\left(\frac{d_i}{d_0}\right)}^n},$

Wherein, a _irepresent the sound-source signal intensity that i-th sonic transducer node receives, G _ithe gain of i-th sensor node, P ₀' be at reference distance d ₀the intensity of place's sound source, d _ifor the distance of target and i-th sensor node, (s _xi, s _yi) and (x, y) be respectively the position coordinates of i-th sensor node and target, n=2 is acoustic signal intensity damped expoential.

4. a kind of wireless sensor network acoustic target iteration localization method as claimed in claim 1, it is characterized in that, aggregation node carries out Sequential processing to each nodes see data, in each iteration, aggregation node only processes the observation data of a sensor node, and other sensor nodes are in resting state.